Complex energy asset such as air conditioner (AC), lighting, and kitchen require regular maintenance to ensure that the energy assets continue to function properly. Often, critical components of these energy assets are more susceptible to failure than other components. In current scenario, earlier detection of energy asset health is not possible except wear & tear. The maintenance of the energy assets will be performed in defined interval. However there is a chance that the energy assets condition would have deteriorated due to extensive usage like coil windings damage & results in magnetic flux linkage & thereby increase in energy consumption. In the case of AC there is a chance that subsystems would have gone bad and the AC may not be able to work effectively. This may get unnoticed and results in higher/lower energy consumption. The failure of the energy assets lead to financial implication in terms of time, effort and production.
However, in current practice in hospitality sector it is difficult to detect outlier of asset such as AC, Lighting, and kitchen asset proactively. In view of the above, earlier detection of asset health is not possible. Therefore, there is need for system and method for predicting the erroneous behavior of the asset.
In view of the above drawbacks, it would be desirable to have methods and systems for predicting erroneous behavior of an energy asset using fourier based clustering technique.